Described is a system for detecting and correcting perception errors in a perception system. In operation, the system generates a list of detected objects from perception data of a scene, which allows for the generation of a list of background classes from backgrounds in the perception data associated with the list of detected objects. For each detected object in the list of detected objects, a closest background class is identified from the list of background classes. Vectors can then be used to determine a semantic feature, which is used to identify axioms. An optimal perception parameter is then generated, which is used to adjust perception parameters in the perception system to minimize perception errors.
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3. The system as set forth in claim 2, wherein the semantic feature is a cosine similarity metric.
4. The system as set forth in claim 2, wherein the semantic feature is a conditional random fields (CRF) feature where co-occurrence statistics are obtained through a probabilistic framework, with a maximum a posteriori probability inference used to determine a likelihood of co-occurring objects.
5. The system as set forth in claim 2, further comprising an operation of causing an autonomous vehicle to initiate a physical operation based on the optimal perception parameter.
8. The computer program product as set forth in claim 7, wherein the semantic feature is a cosine similarity metric.
9. The computer program product as set forth in claim 7, wherein the semantic feature is a conditional random fields (CRF) feature where co-occurrence statistics are obtained through a probabilistic framework, with a maximum a posteriori probability inference used to determine a likelihood of co-occurring objects.
10. The computer program product as set forth in claim 7, further comprising an operation of causing an autonomous vehicle to initiate a physical operation based on the optimal perception parameter.
13. The method as set forth in claim 12, wherein the semantic feature is a cosine similarity metric.
14. The method as set forth in claim 12, wherein the semantic feature is a conditional random fields (CRF) feature where co-occurrence statistics are obtained through a probabilistic framework, with a maximum a posteriori probability inference used to determine a likelihood of co-occurring objects.
15. The method as set forth in claim 12, further comprising an operation of causing an autonomous vehicle to initiate a physical operation based on the optimal perception parameter.
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March 2, 2021
July 4, 2023
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